owanr/SChem5Labels-google-t5-v1_1-large-intra_model-sorted-model_annots_str

SChem5Labels-google-t5-v1_1-large-intra_model

This model is a fine-tuned version of google/t5-v1_1-large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 8.9589

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 200

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	3	29.7549
No log	2.0	6	29.8102
No log	3.0	9	29.6355
25.8256	4.0	12	30.6726
25.8256	5.0	15	30.7965
25.8256	6.0	18	30.7727
26.6306	7.0	21	30.6763
26.6306	8.0	24	30.5263
26.6306	9.0	27	30.9119
26.6258	10.0	30	31.1131
26.6258	11.0	33	31.0140
26.6258	12.0	36	30.8146
26.6258	13.0	39	30.5980
25.0741	14.0	42	30.6435
25.0741	15.0	45	30.0462
25.0741	16.0	48	29.8027
25.4085	17.0	51	29.5555
25.4085	18.0	54	29.7583
25.4085	19.0	57	29.8566
24.1039	20.0	60	29.4801
24.1039	21.0	63	29.4868
24.1039	22.0	66	29.0394
24.1039	23.0	69	29.2099
25.3741	24.0	72	29.0870
25.3741	25.0	75	29.0087
25.3741	26.0	78	28.7179
23.8599	27.0	81	28.5614
23.8599	28.0	84	28.5062
23.8599	29.0	87	28.4833
25.7063	30.0	90	28.0928
25.7063	31.0	93	27.6552
25.7063	32.0	96	27.1825
25.7063	33.0	99	26.9414
25.0888	34.0	102	26.8009
25.0888	35.0	105	26.4969
25.0888	36.0	108	25.8081
24.1641	37.0	111	25.3265
24.1641	38.0	114	24.8796
24.1641	39.0	117	24.0748
23.6238	40.0	120	23.4394
23.6238	41.0	123	22.9299
23.6238	42.0	126	22.3851
23.6238	43.0	129	22.2605
22.53	44.0	132	21.7847
22.53	45.0	135	21.3276
22.53	46.0	138	21.0494
22.1764	47.0	141	20.4277
22.1764	48.0	144	20.0528
22.1764	49.0	147	19.7998
21.3646	50.0	150	19.4783
21.3646	51.0	153	18.8497
21.3646	52.0	156	18.3452
21.3646	53.0	159	17.9207
21.51	54.0	162	17.3766
21.51	55.0	165	16.9509
21.51	56.0	168	16.7865
17.5988	57.0	171	16.7029
17.5988	58.0	174	16.6552
17.5988	59.0	177	16.4299
18.8201	60.0	180	16.2096
18.8201	61.0	183	15.9940
18.8201	62.0	186	15.8806
18.8201	63.0	189	15.7914
18.7912	64.0	192	15.7707
18.7912	65.0	195	15.7896
18.7912	66.0	198	15.7700
16.2629	67.0	201	15.6920
16.2629	68.0	204	15.6683
16.2629	69.0	207	15.5813
15.5326	70.0	210	15.4919
15.5326	71.0	213	15.3044
15.5326	72.0	216	15.1604
15.5326	73.0	219	15.0433
17.0455	74.0	222	14.9565
17.0455	75.0	225	14.8358
17.0455	76.0	228	14.7331
14.6608	77.0	231	14.6237
14.6608	78.0	234	14.5547
14.6608	79.0	237	14.4913
15.2233	80.0	240	14.4341
15.2233	81.0	243	14.3771
15.2233	82.0	246	14.3262
15.2233	83.0	249	14.2761
14.401	84.0	252	14.2134
14.401	85.0	255	14.1438
14.401	86.0	258	14.0946
14.3857	87.0	261	14.0422
14.3857	88.0	264	13.9834
14.3857	89.0	267	13.9312
13.9197	90.0	270	13.8541
13.9197	91.0	273	13.7648
13.9197	92.0	276	13.6514
13.9197	93.0	279	13.5470
13.7735	94.0	282	13.4660
13.7735	95.0	285	13.3741
13.7735	96.0	288	13.2702
13.2372	97.0	291	13.1626
13.2372	98.0	294	13.0221
13.2372	99.0	297	12.8678
12.476	100.0	300	12.7402
12.476	101.0	303	12.6091
12.476	102.0	306	12.4661
12.476	103.0	309	12.3560
12.4134	104.0	312	12.3343
12.4134	105.0	315	12.3986
12.4134	106.0	318	12.4099
12.1218	107.0	321	12.3745
12.1218	108.0	324	12.3192
12.1218	109.0	327	12.2624
12.6856	110.0	330	12.1563
12.6856	111.0	333	12.1173
12.6856	112.0	336	12.0613
12.6856	113.0	339	12.0017
11.8839	114.0	342	11.9240
11.8839	115.0	345	11.8746
11.8839	116.0	348	11.9097
11.52	117.0	351	11.9162
11.52	118.0	354	11.8577
11.52	119.0	357	11.7256
11.6257	120.0	360	11.5926
11.6257	121.0	363	11.5474
11.6257	122.0	366	11.5735
11.6257	123.0	369	11.5936
11.9666	124.0	372	11.4833
11.9666	125.0	375	11.3641
11.9666	126.0	378	11.3100
11.7292	127.0	381	11.2959
11.7292	128.0	384	11.3732
11.7292	129.0	387	11.3367
11.6755	130.0	390	11.1432
11.6755	131.0	393	11.0495
11.6755	132.0	396	11.0216
11.6755	133.0	399	10.9745
11.1861	134.0	402	10.9571
11.1861	135.0	405	10.9238
11.1861	136.0	408	10.8395
10.4923	137.0	411	10.8113
10.4923	138.0	414	10.7781
10.4923	139.0	417	10.7083
11.6983	140.0	420	10.6792
11.6983	141.0	423	10.6387
11.6983	142.0	426	10.5960
11.6983	143.0	429	10.5695
10.4692	144.0	432	10.5019
10.4692	145.0	435	10.4446
10.4692	146.0	438	10.3962
10.3619	147.0	441	10.3448
10.3619	148.0	444	10.2983
10.3619	149.0	447	10.2724
10.2664	150.0	450	10.1874
10.2664	151.0	453	10.1124
10.2664	152.0	456	10.0616
10.2664	153.0	459	9.9878
10.8575	154.0	462	10.0403
10.8575	155.0	465	10.0742
10.8575	156.0	468	9.8701
9.5866	157.0	471	9.8006
9.5866	158.0	474	9.7803
9.5866	159.0	477	9.6946
9.9418	160.0	480	9.6784
9.9418	161.0	483	9.6326
9.9418	162.0	486	9.5806
9.9418	163.0	489	9.5621
9.7861	164.0	492	9.5201
9.7861	165.0	495	9.5372
9.7861	166.0	498	9.6946
9.2799	167.0	501	9.5540
9.2799	168.0	504	9.3983
9.2799	169.0	507	9.3719
8.9886	170.0	510	9.3410
8.9886	171.0	513	9.3396
8.9886	172.0	516	9.3544
8.9886	173.0	519	9.3576
9.4404	174.0	522	9.2906
9.4404	175.0	525	9.2412
9.4404	176.0	528	9.2228
9.4149	177.0	531	9.2118
9.4149	178.0	534	9.1959
9.4149	179.0	537	9.1526
9.3479	180.0	540	9.1415
9.3479	181.0	543	9.1262
9.3479	182.0	546	9.1734
9.3479	183.0	549	9.1700
9.3698	184.0	552	9.1131
9.3698	185.0	555	9.0850
9.3698	186.0	558	9.0757
8.6839	187.0	561	9.0571
8.6839	188.0	564	9.0458
8.6839	189.0	567	9.0427
8.7484	190.0	570	9.0691
8.7484	191.0	573	9.1690
8.7484	192.0	576	9.2576
8.7484	193.0	579	9.1160
8.9591	194.0	582	8.9798
8.9591	195.0	585	8.9571
8.9591	196.0	588	8.9462
9.2337	197.0	591	8.9789
9.2337	198.0	594	9.0140
9.2337	199.0	597	9.0621
9.4757	200.0	600	8.9589

Framework versions

• Transformers 4.34.0
• Pytorch 2.1.0+cu121
• Datasets 2.6.1
• Tokenizers 0.14.1